Start by loading the plotly library
# Import plotly
library(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutdata(diamonds, package= "ggplot2")
str(diamonds)## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...head(diamonds)## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48# Visualizing categorical data
cut_freq <- plot_ly(diamonds, x = ~cut)
#cut_freq
clarity_freq_bycut <- plot_ly(diamonds, x=~cut, color=~clarity)
clarity_freq_bycut## No trace type specified:
## Based on info supplied, a 'histogram' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#histogram# Gapminder dataviz
library(gapminder)
gapminder## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows## Each row represents uniq country and year, with variables of lifeExp, pop, and GDPpercap
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union#head(gapminder)
#str(gapminder)
gapminder <- gapminder
# Convert pop to millions
# Create total gdp field
gapminder2 = gapminder %>%
mutate(tot_gdp = pop*gdpPercap)
gapminder2 = gapminder %>%
mutate(pop_m = pop/1000000)
# Data Visualization
library(ggplot2)
#Scatter plot of two variables, label with country using color
gapminder2007 <- gapminder2 %>%
filter(year == 2007)
gapminder2007## # A tibble: 142 x 7
## country continent year lifeExp pop gdpPercap pop_m
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975. 31.9
## 2 Albania Europe 2007 76.4 3600523 5937. 3.60
## 3 Algeria Africa 2007 72.3 33333216 6223. 33.3
## 4 Angola Africa 2007 42.7 12420476 4797. 12.4
## 5 Argentina Americas 2007 75.3 40301927 12779. 40.3
## 6 Australia Oceania 2007 81.2 20434176 34435. 20.4
## 7 Austria Europe 2007 79.8 8199783 36126. 8.20
## 8 Bahrain Asia 2007 75.6 708573 29796. 0.709
## 9 Bangladesh Asia 2007 64.1 150448339 1391. 150.
## 10 Belgium Europe 2007 79.4 10392226 33693. 10.4
## # ... with 132 more rowspop_bygdp <- ggplot(
gapminder2007 %>%
filter((pop_m > 50) & (pop_m < 1000)),
aes(x = pop_m, y=gdpPercap, color=country)) +
geom_point()
# Make the plot interactive by wrapping it with plotly
plotly_popgdp <- ggplotly(pop_bygdp)
plotly_popgdp